IPF and CPFE - the two different entities or two different presentations of the same disease?

Forced vital capacity trajectories in patients with idiopathic pulmonary fibrosis: a secondary analysis of a multicentre, prospective, observational cohort

BACKGROUND

Idiopathic pulmonary fibrosis is a progressive fibrotic lung disease with a variable clinical trajectory. Decline in forced vital capacity (FVC) is the main indicator of progression; however, missingness prevents long-term analysis of patterns in lung function. We aimed to identify distinct clusters of lung function trajectory among patients with idiopathic pulmonary fibrosis using machine learning techniques.

METHODS

We did a secondary analysis of longitudinal data on FVC collected from a cohort of patients with idiopathic pulmonary fibrosis from the PROFILE study; a multicentre, prospective, observational cohort study. We evaluated the imputation performance of conventional and machine learning techniques to impute missing data and then analysed the fully imputed dataset by unsupervised clustering using self-organising maps. We compared anthropometric features, genomic associations, serum biomarkers, and clinical outcomes between clusters. We also performed a replication of the analysis on data from a cohort of patients with idiopathic pulmonary fibrosis from an independent dataset, obtained from the Chicago Consortium.

FINDINGS

415 (71%) of 581 participants recruited into the PROFILE study were eligible for further analysis. An unsupervised machine learning algorithm had the lowest imputation error among tested methods, and self-organising maps identified four distinct clusters (1-4), which was confirmed by sensitivity analysis. Cluster 1 comprised 140 (34%) participants and was associated with a disease trajectory showing a linear decline in FVC over 3 years. Cluster 2 comprised 100 (24%) participants and was associated with a trajectory showing an initial improvement in FVC before subsequently decreasing. Cluster 3 comprised 113 (27%) participants and was associated with a trajectory showing an initial decline in FVC before subsequent stabilisation. Cluster 4 comprised 62 (15%) participants and was associated with a trajectory showing stable lung function. Median survival was shortest in cluster 1 (2·87 years [IQR 2·29-3·40]) and cluster 3 (2·23 years [1·75-3·84]), followed by cluster 2 (4·74 years [3·96-5·73]), and was longest in cluster 4 (5·56 years [5·18-6·62]). Baseline FEV1 to FVC ratio and concentrations of the biomarker SP-D were significantly higher in clusters 1 and 3. Similar lung function clusters with some shared anthropometric features were identified in the replication cohort.

INTERPRETATION

Using a data-driven unsupervised approach, we identified four clusters of lung function trajectory with distinct clinical and biochemical features. Enriching or stratifying longitudinal spirometric data into clusters might optimise evaluation of intervention efficacy during clinical trials and patient management.

FUNDING

National Institute for Health and Care Research, Medical Research Council, and GlaxoSmithKline.

Use of machine learning models to predict prognosis of combined pulmonary fibrosis and emphysema in a Chinese population

Background

Combined pulmonary fibrosis and emphysema (CPFE) is a novel clinical entity with a poor prognosis. This study aimed to develop a clinical nomogram model to predict the 1-, 2- and 3-year mortality of patients with CPFE by using the machine learning approach, and to validate the predictive ability of the interstitial lung disease-gender-age-lung physiology (ILD-GAP) model in CPFE.

Methods

The data of CPFE patients from January 2015 to October 2021 who met the inclusion criteria were retrospectively collected. We utilized LASSO regression and multivariable Cox regression analysis to identify the variables associated with the prognosis of CPFE and generate a nomogram. The Harrell's C index, the calibration curve and the area under the receiver operating characteristic (ROC) curve (AUC) were used to evaluate the performance of the nomogram. Then, we performed likelihood ratio test, net reclassification improvement (NRI), integrated discrimination improvement (IDI) and decision curve analysis (DCA) to compare the performance of the nomogram with that of the ILD-GAP model.

Results

A total of 184 patients with CPFE were enrolled. During the follow-up, 90 patients died. After screening out, diffusing lung capacity for carbon monoxide (DLCO), right ventricular diameter (RVD), C-reactive protein (CRP), and globulin were found to be associated with the prognosis of CPFE. The nomogram was then developed by incorporating the above five variables, and it showed a good performance, with a Harrell's C index of 0.757 and an AUC of 0.800 (95% CI 0.736–0.863). Moreover, the calibration plot of the nomogram showed good concordance between the prediction probabilities and the actual observations. The nomogram also improved the discrimination ability of the ILD-GAP model compared to that of the ILD-GAP model alone, and this was substantiated by the likelihood ratio test, NRI and IDI. The significant clinical utility of the nomogram was demonstrated by DCA.

Conclusion

Age, DLCO, RVD, CRP and globulin were identified as being significantly associated with the prognosis of CPFE in our cohort. The nomogram incorporating the 5 variables showed good performance in predicting the mortality of CPFE. In addition, although the nomogram was superior to the ILD-GAP model in the present cohort, further validation is needed to determine the clinical utility of the nomogram.

Prognostic Significance of Red Cell Distribution Width in Idiopathic Pulmonary Fibrosis and Combined Pulmonary Fibrosis Emphysema

OBJECTIVE

The red cell distribution width (RDW) is an inexpensive, readily available prognostic indicator of several diseases. RDW has been assessed as a prognostic biomarker in patients with idiopathic pulmonary fibrosis (IPF) in only one study; furthermore, the relationship between the RDW and combined pulmonary fibrosis emphysema (CPFE) has yet to be reported.

SUBJECTS AND METHODS

This single-center study was conducted between January 2015 and December 2018 in the Atatürk Chest Diseases and Chest Surgery Education and Research Hospital. Baseline characteristics, laboratory results, and survival status of patients were recorded.

RESULTS

The RDW value was significantly higher in the CPFE group than in the IPF group (median [IQR 25-75]; 16.8 [15.5-19] vs. 15.3 [13.7-16.8], p = 0.028). High RDW values were correlated with carbon monoxide diffusion capacity (DLCO) (r: -0.653 p = 0.001), 6-minute walking test (6MWT) distance (r: -0.361 p = 0.017), arterial partial oxygen pressure (PaO2) (r: -0.692 p < 0.001), and systolic pulmonary arterial pressure (SPAP) (r: 0.349 p = 0.022) in patients with fibrotic lung disease. The RDW value was significantly higher in the exitus group than in the survivors (median [IQR 25-75]; 18.4 [15.4-19] vs. 15.2 [13.5-17.2], p = 0.016). A univariate Cox regression analysis identified DLCO, SPAP, PaO2, and RDW as potential covariates of mortality. In a multivariate analysis, the DLCO (HR 1.21, 95% CI 1.11-1.47, p = 0.012) and RDW level (HR 1.65, 95% CI 1.09-2.47, p = 0.023) remained independent predictors of mortality.

CONCLUSION

High RDW values appear to be a simple prognostic factor in patients with IPF or CPFE.